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The Piccolo Triglycerides - Capillary Test System used with the Piccolo xpress Chemistry Analyzer is intended for the in vitro quantitative determination of triglycerides in capillary (fingerstick) heparinized whole blood in a clinical laboratory setting or point-of-care location.

Triglyceride measurements are used in the diagnosis and treatment of patients with diabetes mellitus, nephrosis, liver obstruction, and other diseases involving lipid metabolism, or various endocrine disorders.

The Piccolo® Lipid Panel - Capillary Reagent Disc (which contains the Piccolo® Triglycerides - Capillary Test System) is designed to separate a heparinized whole blood sample into plasma and blood cells. The disc meters the required quantity of plasma and diluent, mixes the plasma with diluent, and delivers the mixture to the reaction cuvettes along the disc perimeter. The diluted plasma mixes with the reagent beads, initiating the chemical reactions that are then monitored by the analyzer.

Here's a breakdown of the acceptance criteria and the study details for the Piccolo® Triglycerides - Capillary Test System, based on the provided 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are not explicitly stated as distinct thresholds in this summary. Instead, the summary presents the performance characteristics and implies that these results demonstrate substantial equivalence to predicate devices. For this table, I'll present the performance characteristics provided.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size for Linearity: Not explicitly stated, but the "Summary of Linearity" implies sufficient samples were used to generate the reported slope, intercept, and correlation coefficient.
• Sample Size for Precision: 160 observations (n=160) for both within-run and total precision for each serum level.
• Sample Size for Method Comparison: 588 samples (N=588).
• Data Provenance: Not explicitly stated (e.g., country of origin). The data is from "clinical and non-clinical tests performed using the Piccolo® Triglycerides Test System," implying it was generated specifically for this submission. It's prospective testing as the device underwent testing.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications of Those Experts

This information is not applicable to an in vitro diagnostic (IVD) device that measures a biomarker. The "ground truth" for these tests is typically established through reference methods or highly accurate laboratory analyzers, not expert interpretation. In this case, the Roche Triglycerides Test on the Cobas 6000 Analyzer serves as the comparator or reference method for the method comparison study.

4. Adjudication Method for the Test Set

Not applicable for this type of IVD device. Test results are quantitative measurements, not subjective interpretations requiring adjudication.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs Without AI Assistance

Not applicable. This is an in vitro diagnostic device for quantitative blood analysis, not an AI-assisted diagnostic imaging or interpretation system involving human readers.

6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) Was Done

Yes, the performance data presented (linearity, precision, method comparison) represents the standalone performance of the Piccolo® Triglycerides - Capillary Test System. It measures triglyceride levels quantitatively without direct human interpretation of the primary result (beyond initiating the test and reading the numerical output).

7. The Type of Ground Truth Used

For the method comparison study, the "ground truth" (or reference method) was the Roche Triglycerides Test performed on the Cobas 6000 Analyzer, with results reported as the "Average of Duplicates."

8. The Sample Size for the Training Set

Not applicable. This device is a diagnostic test kit and analyzer, not a machine learning or AI algorithm that requires a "training set." Its calibration is based on "Bar code with factory calibrated lot specific data" (Table 1), implying chemical calibration and quality control rather than data-driven machine learning training.

9. How the Ground Truth for the Training Set Was Established

Not applicable, as there is no "training set" in the context of an AI/ML algorithm. The calibration of the device is factory-set and lot-specific using bar codes, as noted in Table 1.

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